Strategic planning and multi dimensional knapsack problem
Here is a speculative cast of the problem to a travelling salesman problem, which would lead to shortest-path algorithms.

Please note this idea suggests different constraints to explore.


Given the knowledge vectors and efforts, build a acyclic directed graph (acyclic, as we are not supposed to unlearn). A vertex is an article, represented by its knowledge vector. An edge links two articles, weighted by the effort to "move" to the target article/vertex (i.e. acquire the knowledge of that article).
Assign a zero vector to a new participant. That is the starting point on the graph is vertex V0 = (0, ..., 0).
Define a learning objective as a vector V.
Use a shortest-path algorithm to find a (V0, V) plan.


This procedure is insufficient, as there are many ways to build the graph (in other words, the above is completely pointless as is). Extra constraints are necessary to make it practical. For example, we can order the vertices by ordering them along each dimension. Such setting would lead learners to start with "easy" articles (V[i] is low), and move step by step toward more complex topics ((V[i] gets higher).

The graph construction depends on the data available. For example, are knowledge vectors "absolute", or can they be relative? Relative can help in creating a path, as moving from V to W requires an effort that depends on your learner's initial conditions (V0 may not be 0 everywhere, afterall).



Is it an AI question? Definitely.